We present HowSumm, a novel large-scale dataset for the task of query-focused
multi-document summarization (qMDS), which targets the use-case of generating
actionable instructions from a set of sources. This use-case is different from
the use-cases covered in existing multi-document summarization (MDS) datasets
and is applicable to educational and industrial scenarios. We employed
automatic methods, and leveraged statistics from existing human-crafted qMDS
datasets, to create HowSumm from wikiHow website articles and the sources they
cite. We describe the creation of the dataset and discuss the unique features
that distinguish it from other summarization corpora. Automatic and human
evaluations of both extractive and abstractive summarization models on the
dataset reveal that there is room for improvement.

本篇文章提出了 HowSumm 这一全新的数据集，可用于针对查询的多文档摘要 (qMDS) 任务，其目标是从一组来源生成可操作的指令。通过从 wikiHow 网站文章和它们引用的来源中提取数据创建了该数据集，并讨论了与其他摘要语料库的不同之处，实验结果表明，提取式和抽象式摘要模型在该数据集上的性能仍有提升空间。

HowSumm：基于 WikiHow 文章的多文档自动摘要数据集

HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow  Articles

The progress in Query-focused Multi-Document Summarization (QMDS) has been
limited by the lack of sufficient largescale high-quality training datasets. We
present two QMDS training datasets, which we construct using two data
augmentation methods: (1) transferring the commonly used single-document
CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2)
mining search-query logs to create the QMDSIR dataset. These two datasets have
complementary properties, i.e., QMDSCNN has real summaries but queries are
simulated, while QMDSIR has real queries but simulated summaries. To cover both
these real summary and query aspects, we build abstractive end-to-end neural
network models on the combined datasets that yield new state-of-the-art
transfer results on DUC datasets. We also introduce new hierarchical encoders
that enable a more efficient encoding of the query together with multiple
documents. Empirical results demonstrate that our data augmentation and
encoding methods outperform baseline models on automatic metrics, as well as on
human evaluations along multiple attributes.

该研究介绍了两个 QMDS 训练数据集，这些数据集具有互补的性质并使用新的层次编码器建立了基于端的神经网络模型，这些模型不仅在自动度量上表现出了优异的结果，而且在人类评估中也表现出了优异的结果。